Check pCVP2-BRI1-mCitrine specificity in scRNA-seq¶

Here we perform analysis against the custom reference. The results are exported and inlcuded in the SI table.

In [1]:
library(tidyverse)
library(Seurat)
library(cowplot)
library(ComplexHeatmap)
library(circlize)
library(GeneOverlap)
library(gprofiler2)
library(ggrepel)
library(ggplot2)
library(muscat)
library(purrr)
library(limma)
library(scran)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.0     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.1     ✔ tibble    3.1.8
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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========================================
ComplexHeatmap version 2.14.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite either one:
- Gu, Z. Complex Heatmap Visualization. iMeta 2022.
- Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
    genomic data. Bioinformatics 2016.


The new InteractiveComplexHeatmap package can directly export static 
complex heatmaps into an interactive Shiny app with zero effort. Have a try!

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
========================================


========================================
circlize version 0.4.15
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================


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    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
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Welcome to Bioconductor

    Vignettes contain introductory material; view with
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    'citation("Biobase")', and for packages 'citation("pkgname")'.



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Loading required package: scuttle

In [2]:
library(future)
#for 200gb ram 
options(future.globals.maxSize = 200000 * 1024^2)
In [3]:
sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: AlmaLinux 9.3 (Shamrock Pampas Cat)

Matrix products: default
BLAS/LAPACK: /hpc/group/pbenfeylab/tmn23/miniconda3/envs/muscat/lib/libopenblasp-r0.3.21.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    grid      stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] future_1.31.0               scran_1.26.0               
 [3] scuttle_1.8.0               SingleCellExperiment_1.20.0
 [5] SummarizedExperiment_1.28.0 Biobase_2.58.0             
 [7] GenomicRanges_1.50.0        GenomeInfoDb_1.34.8        
 [9] IRanges_2.32.0              S4Vectors_0.36.0           
[11] BiocGenerics_0.44.0         MatrixGenerics_1.10.0      
[13] matrixStats_0.63.0          limma_3.54.0               
[15] muscat_1.12.0               ggrepel_0.9.3              
[17] gprofiler2_0.2.1            GeneOverlap_1.34.0         
[19] circlize_0.4.15             ComplexHeatmap_2.14.0      
[21] cowplot_1.1.1               SeuratObject_4.1.3         
[23] Seurat_4.3.0                lubridate_1.9.2            
[25] forcats_1.0.0               stringr_1.5.0              
[27] dplyr_1.1.0                 purrr_1.0.1                
[29] readr_2.1.4                 tidyr_1.3.0                
[31] tibble_3.1.8                ggplot2_3.4.1              
[33] tidyverse_2.0.0            

loaded via a namespace (and not attached):
  [1] pbdZMQ_0.3-9              scattermore_0.8          
  [3] bit64_4.0.5               irlba_2.3.5.1            
  [5] DelayedArray_0.24.0       data.table_1.14.8        
  [7] KEGGREST_1.38.0           RCurl_1.98-1.10          
  [9] doParallel_1.0.17         generics_0.1.3           
 [11] ScaledMatrix_1.6.0        RhpcBLASctl_0.23-42      
 [13] RSQLite_2.2.20            RANN_2.6.1               
 [15] bit_4.0.5                 tzdb_0.3.0               
 [17] spatstat.data_3.0-0       httpuv_1.6.9             
 [19] viridis_0.6.2             hms_1.1.2                
 [21] evaluate_0.20             promises_1.2.0.1         
 [23] fansi_1.0.4               progress_1.2.2           
 [25] caTools_1.18.2            igraph_1.3.5             
 [27] DBI_1.1.3                 geneplotter_1.76.0       
 [29] htmlwidgets_1.6.1         spatstat.geom_3.0-6      
 [31] ellipsis_0.3.2            backports_1.4.1          
 [33] annotate_1.76.0           aod_1.3.2                
 [35] deldir_1.0-6              sparseMatrixStats_1.10.0 
 [37] vctrs_0.5.2               ROCR_1.0-11              
 [39] abind_1.4-5               cachem_1.0.6             
 [41] withr_2.5.0               progressr_0.13.0         
 [43] sctransform_0.3.5         prettyunits_1.1.1        
 [45] goftest_1.2-3             cluster_2.1.4            
 [47] IRdisplay_1.1             lazyeval_0.2.2           
 [49] crayon_1.5.2              genefilter_1.80.0        
 [51] spatstat.explore_3.0-6    edgeR_3.40.0             
 [53] pkgconfig_2.0.3           nlme_3.1-162             
 [55] vipor_0.4.5               blme_1.0-5               
 [57] rlang_1.0.6               globals_0.16.2           
 [59] lifecycle_1.0.3           miniUI_0.1.1.1           
 [61] rsvd_1.0.5                polyclip_1.10-4          
 [63] lmtest_0.9-40             Matrix_1.5-3             
 [65] IRkernel_1.3.2            boot_1.3-28.1            
 [67] zoo_1.8-11                base64enc_0.1-3          
 [69] beeswarm_0.4.0            ggridges_0.5.4           
 [71] GlobalOptions_0.1.2       png_0.1-8                
 [73] viridisLite_0.4.1         rjson_0.2.21             
 [75] bitops_1.0-7              KernSmooth_2.23-20       
 [77] Biostrings_2.66.0         blob_1.2.3               
 [79] DelayedMatrixStats_1.20.0 shape_1.4.6              
 [81] parallelly_1.34.0         spatstat.random_3.1-3    
 [83] beachmat_2.14.0           scales_1.2.1             
 [85] memoise_2.0.1             magrittr_2.0.3           
 [87] plyr_1.8.8                ica_1.0-3                
 [89] gplots_3.1.3              zlibbioc_1.44.0          
 [91] compiler_4.2.2            dqrng_0.3.0              
 [93] RColorBrewer_1.1-3        clue_0.3-64              
 [95] lme4_1.1-31               DESeq2_1.38.0            
 [97] fitdistrplus_1.1-8        cli_3.6.0                
 [99] XVector_0.38.0            lmerTest_3.1-3           
[101] listenv_0.9.0             patchwork_1.1.2          
[103] pbapply_1.7-0             TMB_1.9.2                
[105] MASS_7.3-58.2             tidyselect_1.2.0         
[107] stringi_1.7.12            BiocSingular_1.14.0      
[109] locfit_1.5-9.7            tools_4.2.2              
[111] timechange_0.2.0          future.apply_1.10.0      
[113] parallel_4.2.2            uuid_1.1-0               
[115] bluster_1.8.0             foreach_1.5.2            
[117] metapod_1.6.0             gridExtra_2.3            
[119] Rtsne_0.16                digest_0.6.31            
[121] shiny_1.7.4               Rcpp_1.0.10              
[123] broom_1.0.3               later_1.3.0              
[125] RcppAnnoy_0.0.20          httr_1.4.4               
[127] AnnotationDbi_1.60.0      Rdpack_2.4               
[129] colorspace_2.1-0          XML_3.99-0.13            
[131] tensor_1.5                reticulate_1.28          
[133] splines_4.2.2             statmod_1.5.0            
[135] uwot_0.1.14               spatstat.utils_3.0-1     
[137] scater_1.26.0             sp_1.6-0                 
[139] plotly_4.10.1             xtable_1.8-4             
[141] jsonlite_1.8.4            nloptr_2.0.3             
[143] R6_2.5.1                  pillar_1.8.1             
[145] htmltools_0.5.4           mime_0.12                
[147] glue_1.6.2                fastmap_1.1.0            
[149] minqa_1.2.5               BiocParallel_1.32.5      
[151] BiocNeighbors_1.16.0      codetools_0.2-19         
[153] utf8_1.2.3                lattice_0.20-45          
[155] spatstat.sparse_3.0-0     numDeriv_2016.8-1.1      
[157] pbkrtest_0.5.2            ggbeeswarm_0.7.1         
[159] leiden_0.4.3              gtools_3.9.4             
[161] survival_3.5-3            glmmTMB_1.1.5            
[163] repr_1.1.6                munsell_0.5.0            
[165] GetoptLong_1.0.5          GenomeInfoDbData_1.2.9   
[167] iterators_1.0.14          variancePartition_1.28.0 
[169] reshape2_1.4.4            gtable_0.3.1             
[171] rbibutils_2.2.13         
In [4]:
rc.integrated <- readRDS("../../CheWei/scRNA-seq/Integrated_Objects/rc.integrated_8S_CVP_BRI1_seu3_annotated_20230316.rds")
In [5]:
rc.integrated
An object of class Seurat 
71788 features across 55427 samples within 3 assays 
Active assay: SCT (25288 features, 0 variable features)
 2 other assays present: RNA, integrated
 4 dimensional reductions calculated: pca, umap, umap_3D, umap_2D
In [6]:
rc.integrated <- subset(rc.integrated, 
                        subset = orig.ident %in% c("sc_130",
                                              "sc_131",
                                              "sc_132",
                                              "sc_134",
                                              "sc_135",
                                              "sc_136"))
In [7]:
table(rc.integrated$orig.ident, rc.integrated$geno)
        
           WT bri1_T pCVP2_BRI1_Citrine_bri1_T
  sc_130 6589      0                         0
  sc_131    0   7615                         0
  sc_132    0      0                      6550
  sc_134 7745      0                         0
  sc_135    0   5038                         0
  sc_136    0      0                      6089
In [8]:
feature_names <- read_tsv("../data/features.tsv.gz", col_names = c("AGI", "Name", "Type")) %>%
  select(-Type) %>%
  distinct()
Rows: 32833 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (3): AGI, Name, Type

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
In [9]:
rc.integrated$geno <- factor(rc.integrated$geno, levels=c("WT", "bri1_T", "pCVP2_BRI1_Citrine_bri1_T"))

Cell and developmental stage metadata¶

  • Developmental stage: time_zone
  • Cell type:cell_type
  • Combination of cell type and developmental stage: time_zone_cell_type
In [10]:
order <- c("Quiescent Center", "Ground Tissue","Columella", "Lateral Root Cap", "Atrichoblast", "Trichoblast", "Cortex", "Endodermis", "Phloem","Protophloem", "Xylem", "Procambium","Pericycle","Phloem Pole Pericycle", "Protoxylem", "Metaxylem", "Unknown")
palette <- c("#9400D3", "#DCD0FF","#5AB953", "#BFEF45", "#008080", "#21B6A8", "#82B6FF", "#0000FF","#E6194B", "#DD77EC", "#9A6324", "#FFE119", "#FF9900", "#FFD4E3", "#9A6324", "#DDAA6F", "#EEEEEE")
rc.integrated$cell_type <- factor(rc.integrated$cell_type, levels = order[sort(match(unique(rc.integrated$cell_type),order))])
color <- palette[sort(match(unique(rc.integrated$cell_type),order))]
In [11]:
options(repr.plot.width=16.5, repr.plot.height=6)
(Celltype_umap <- DimPlot(rc.integrated, 
                      reduction = "umap", 
                      group.by = "cell_type", 
                      cols = color, split.by = 'geno', 
                      ncol=3, 
                      pt.size = 0.5))

ggsave("../output/CVP/Cell_type_umap_square_all_samples.pdf", width=16.5, height=6)
In [12]:
options(repr.plot.width = 18, repr.plot.height = 6)

DefaultAssay(rc.integrated) <- "SCT"
# expression of the transgene
FeaturePlot(rc.integrated, features="BRI1-mCitrine", split.by = "geno", order=T, max.cutoff = "q90", pt.size = 0.5)

ggsave("../output/CVP/BRI1-mCitrine_expression_all_samples.pdf", width=18.3, height=6)
Warning message in FeaturePlot(rc.integrated, features = "BRI1-mCitrine", split.by = "geno", :
“All cells have the same value (0) of BRI1-mCitrine.”
In [13]:
# expression of CVP2
options(repr.plot.width = 18, repr.plot.height = 6)

DefaultAssay(rc.integrated) <- "SCT"
# expression of the transgene
FeaturePlot(rc.integrated, features="AT1G05470", split.by = "geno", order=T, max.cutoff = "q90", pt.size = 0.5)

ggsave("../output/CVP/CVP2_AT1G05470_expression_all_samples.pdf", width=18.3, height=6)
In [14]:
options(repr.plot.width=30, repr.plot.height=7)

DimPlot(rc.integrated, reduction = "umap", group.by = "cell_type", cols = color, split.by = 'orig.ident', pt.size = 0.75, ncol=8)
In [15]:
DefaultAssay(rc.integrated) <- "SCT"
# expression of the transgene
FeaturePlot(rc.integrated, features="BRI1-mCitrine", split.by = "orig.ident", order=T, max.cutoff = "q80", pt.size = 0.5)
Warning message in FeaturePlot(rc.integrated, features = "BRI1-mCitrine", split.by = "orig.ident", :
“All cells have the same value (0) of BRI1-mCitrine.”
Warning message in FeaturePlot(rc.integrated, features = "BRI1-mCitrine", split.by = "orig.ident", :
“All cells have the same value (0) of BRI1-mCitrine.”
In [16]:
DefaultAssay(rc.integrated) <- "SCT"
# expression of the transgene
FeaturePlot(rc.integrated, features="AT1G05470", split.by = "orig.ident", order=T, max.cutoff = "q80", pt.size = 0.5)
In [17]:
table(rc.integrated$orig.ident, rc.integrated$cell_type)
        
         Columella Lateral Root Cap Atrichoblast Trichoblast Cortex Endodermis
  sc_130       895             1058         1445        1000    972        725
  sc_131       672             1800         1441         900    777        490
  sc_132       474             1597         1089         916    720        544
  sc_134       582             2101         1171        1041    906        573
  sc_135       294              991         1055         707    544        480
  sc_136       649             1240         1176         871    878        617
        
         Phloem Xylem Procambium Pericycle
  sc_130     52   172        127       143
  sc_131    130   273        320       812
  sc_132    138   230        288       554
  sc_134    123   267        348       633
  sc_135     88   191        260       428
  sc_136     78   147        186       247
In [18]:
table(rc.integrated$orig.ident, rc.integrated$time_zone)
        
         Distal Columella Distal Lateral Root Cap Elongation Maturation
  sc_130              834                     699       2433       1206
  sc_131              626                     566       1873       1430
  sc_132              442                     678       1950       1511
  sc_134              535                     971       2472       1673
  sc_135              268                     201       1110        930
  sc_136              610                     581       2009       1302
        
         Meristem Proximal Columella Proximal Lateral Root Cap
  sc_130      998                 60                       359
  sc_131     1887                 36                      1197
  sc_132     1023                 31                       915
  sc_134      921                 44                      1129
  sc_135     1730                 26                       773
  sc_136      891                 37                       659

Convert to sce¶

In [19]:
#  construct sce manually
my_metadata <- data.frame(sample_id = rc.integrated$orig.ident,
                              group_id = rc.integrated$geno,
                              cluster_id = rc.integrated$cell_type, 
                             date=rc.integrated$rep) # include experimental rep as co-variate

sce <- SingleCellExperiment(assays = list(counts = rc.integrated@assays$RNA@counts),
	                            colData = my_metadata)
In [20]:
(sce <- prepSCE(sce, 
        kid = "cluster_id", # subpopulation assignments
        gid = "group_id",   # group IDs (ctrl/stim)
        sid = "sample_id",    # sample IDs (ctrl/stim.1234)
        drop = FALSE))        # drop all other colData columns
class: SingleCellExperiment 
dim: 28980 39626 
metadata(1): experiment_info
assays(1): counts
rownames(28980): AT1G01010 AT1G01020 ... AT5G39430 AT5G54075
rowData names(0):
colnames(39626): AAACCCACAACAGCCC_6 AAACCCACAGAGTAAT_6 ...
  TTTGTTGCATCGATAC_11 TTTGTTGTCAAGAGGC_11
colData names(4): cluster_id sample_id group_id date
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

pre-filtering¶

In [21]:
# remove undetected genes
sce <- sce[rowSums(counts(sce) > 0) > 0, ]
dim(sce)
  1. 26993
  2. 39626
In [22]:
# create pseudobulk profiles
pb <- aggregateData(sce,
    assay = "counts", fun = "sum",
    by = c("cluster_id", "sample_id"))
# one sheet per subpopulation
assayNames(pb)
  1. 'Columella'
  2. 'Lateral Root Cap'
  3. 'Atrichoblast'
  4. 'Trichoblast'
  5. 'Cortex'
  6. 'Endodermis'
  7. 'Phloem'
  8. 'Xylem'
  9. 'Procambium'
  10. 'Pericycle'
In [23]:
# pseudobulks for 1st subpopulation
t(head(assay(pb)))
A matrix: 6 × 6 of type dbl
AT1G01010AT1G01020AT1G03987AT1G01030AT1G01040AT1G01050
sc_1306897121191846
sc_1314692010171092
sc_132325201010 901
sc_1344673018141210
sc_135145701110 623
sc_1365581019161440
In [24]:
# experiment info for contrasts, add dates from csv

ei <- metadata(sce)$experiment_info

ei

ei$rep <- c(1, 1, 1, 2, 2, 2)
ei
#sample_date <- select(bscs, sample_id=sample, date=rep)

#ei <- left_join(ei, sample_date)

#ei
A data.frame: 6 × 3
sample_idgroup_idn_cells
<fct><fct><dbl>
sc_130WT 6589
sc_131bri1_T 7615
sc_132pCVP2_BRI1_Citrine_bri1_T6550
sc_134WT 7745
sc_135bri1_T 5038
sc_136pCVP2_BRI1_Citrine_bri1_T6089
A data.frame: 6 × 4
sample_idgroup_idn_cellsrep
<fct><fct><dbl><dbl>
sc_130WT 65891
sc_131bri1_T 76151
sc_132pCVP2_BRI1_Citrine_bri1_T65501
sc_134WT 77452
sc_135bri1_T 50382
sc_136pCVP2_BRI1_Citrine_bri1_T60892
In [25]:
mm <- model.matrix(~ 0 + ei$group_id + ei$rep)
dimnames(mm) <- list(ei$sample_id, c(levels(ei$group_id), "rep"))

mm
A matrix: 6 × 4 of type dbl
WTbri1_TpCVP2_BRI1_Citrine_bri1_Trep
sc_1301001
sc_1310101
sc_1320011
sc_1341002
sc_1350102
sc_1360012
In [26]:
contrast <- makeContrasts("pCVP2_BRI1_Citrine_bri1_T-bri1_T", 
                            "pCVP2_BRI1_Citrine_bri1_T-WT", levels = mm)

contrast
A matrix: 4 × 2 of type dbl
pCVP2_BRI1_Citrine_bri1_T-bri1_TpCVP2_BRI1_Citrine_bri1_T-WT
WT 0-1
bri1_T-1 0
pCVP2_BRI1_Citrine_bri1_T 1 1
rep 0 0
In [27]:
res <- pbDS(pb, design = mm, 
            contrast = contrast, 
            method="edgeR", 
            min_cells=5, 
            filter = c("none"))
  |======================================================================| 100%

DEG results¶

In [28]:
# DEG results with gene freqs
(res_to_write_frq <- resDS(sce, res, bind = "row", cpm=TRUE, frq=T))
A data.frame: 465062 × 24
genecluster_idsc_130.cpmsc_134.cpmsc_131.cpmsc_135.cpmsc_132.cpmsc_136.cpmsc_130.frqsc_134.frq⋯WT.frqbri1_T.frqpCVP2_BRI1_Citrine_bri1_T.frqlogFClogCPMFp_valp_adj.locp_adj.glbcontrast
<chr><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><chr>
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AT1G01010Columella 9.860 9.66 8.300 4.610 9.790 10.2000.072600.06870⋯0.0711000.061100.07480 0.057900 3.22004.42e-020.833001.00001.0000pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01020Columella 14.100 15.30 16.600 18.800 15.900 15.0000.096100.10300⋯0.0988000.137000.09880-0.193000 4.02006.53e-010.419000.86400.7690pCVP2_BRI1_Citrine_bri1_T-bri1_T
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AT1G03987Columella 0.145 0.00 0.000 0.000 0.000 0.0000.001120.00000⋯0.0006770.000000.00000-1.760000-1.15002.16e-010.642001.00000.9950pCVP2_BRI1_Citrine_bri1_T-WT
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AT1G01030Columella 3.050 3.78 1.800 3.620 3.060 3.5200.022300.02750⋯0.0244000.019700.02320-0.076900 1.81003.08e-020.861001.00001.0000pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01040Columella 2.760 2.94 3.070 3.290 3.060 2.9700.020100.02410⋯0.0217000.025900.01960-0.078900 1.77002.77e-020.868001.00000.9800pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01040Columella 2.760 2.94 3.070 3.290 3.060 2.9700.020100.02410⋯0.0217000.025900.01960 0.076100 1.77002.75e-020.868001.00001.0000pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01050Columella268.000254.00197.000205.000276.000267.0000.768000.72700⋯0.7520000.735000.76800 0.433000 7.94009.27e+000.002330.03670.0362pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01050Columella268.000254.00197.000205.000276.000267.0000.768000.72700⋯0.7520000.735000.76800 0.056500 7.94001.61e-010.688001.00000.9980pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01060Columella 0.435 0.84 0.722 0.988 0.306 0.5560.003350.00687⋯0.0047400.007250.00356-0.975000 0.05635.80e-010.446000.86400.7870pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01060Columella 0.435 0.84 0.722 0.988 0.306 0.5560.003350.00687⋯0.0047400.007250.00356-0.510000 0.05631.60e-010.689001.00000.9980pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01070Columella 0.145 0.21 1.440 0.329 0.612 0.1850.001120.00172⋯0.0013500.009320.00178-1.160000-0.15106.59e-010.417000.86400.7680pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01070Columella 0.145 0.21 1.440 0.329 0.612 0.1850.001120.00172⋯0.0013500.009320.00178 1.120000-0.15104.07e-010.524001.00000.9810pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01080Columella 0.435 0.42 0.722 0.658 0.306 0.3710.003350.00344⋯0.0033900.006210.00267-0.960000-0.15204.20e-010.517000.88200.8020pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01080Columella 0.435 0.42 0.722 0.658 0.306 0.3710.003350.00344⋯0.0033900.006210.00267-0.291000-0.15203.71e-020.847001.00001.0000pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01090Columella 38.000 39.10 41.700 41.500 38.300 38.8000.242000.25900⋯0.2490000.289000.25600-0.111000 5.32003.70e-010.543000.89200.8080pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01090Columella 38.000 39.10 41.700 41.500 38.300 38.8000.242000.25900⋯0.2490000.289000.25600-0.000277 5.32002.37e-060.999001.00001.0000pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01100Columella267.000297.00407.000492.000325.000343.0000.746000.76500⋯0.7540000.895000.81700-0.423000 8.47009.84e+000.001710.02900.0293pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01100Columella267.000297.00407.000492.000325.000343.0000.746000.76500⋯0.7540000.895000.81700 0.246000 8.47003.33e+000.068201.00000.8040pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01110Columella 2.180 2.52 2.160 0.658 3.060 1.6700.016800.01890⋯0.0176000.013500.01600 0.623000 1.31001.05e+000.305000.78900.6810pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01110Columella 2.180 2.52 2.160 0.658 3.060 1.6700.016800.01890⋯0.0176000.013500.01600-0.015900 1.31008.72e-040.976001.00001.0000pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01120Columella 2.030 3.15 1.980 0.658 3.670 2.9700.014500.02410⋯0.0183000.012400.01870 1.170000 1.51004.42e+000.035600.26200.2120pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01120Columella 2.030 3.15 1.980 0.658 3.670 2.9700.014500.02410⋯0.0183000.012400.01870 0.373000 1.51005.95e-010.440001.00000.9810pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01130Columella 0.435 1.26 1.080 0.658 1.840 0.9270.003350.01030⋯0.0060900.008280.00980 0.508000 0.47703.13e-010.576000.89200.8250pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01130Columella 0.435 1.26 1.080 0.658 1.840 0.9270.003350.01030⋯0.0060900.008280.00980 0.702000 0.47706.49e-010.420001.00000.9810pCVP2_BRI1_Citrine_bri1_T-WT
AT1G01140Columella 24.200 16.20 14.100 9.220 12.900 18.9000.160000.11700⋯0.1430000.100000.12000 0.482000 4.07003.76e+000.052400.33200.2670pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G01140Columella 24.200 16.20 14.100 9.220 12.900 18.9000.160000.11700⋯0.1430000.100000.12000-0.290000 4.07001.62e+000.202001.00000.9810pCVP2_BRI1_Citrine_bri1_T-WT
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
AT1G49205Pericycle00.294000.000000.579000.00790⋯0.006440.0000000.00749 4.6900-1.724.54e+000.07480.4970.329pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT1G49205Pericycle00.294000.000000.579000.00790⋯0.006440.0000000.00749 0.8970-1.724.59e-010.52200.9010.981pCVP2_BRI1_Citrine_bri1_T-WT
AT2G07975Pericycle00.058800.000000.000000.00158⋯0.001290.0000000.00000 0.0461-2.553.22e-111.00001.0001.000pCVP2_BRI1_Citrine_bri1_T-bri1_T
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AT3G53070Pericycle00.058800.000000.000000.00158⋯0.001290.0000000.00000 0.0461-2.553.22e-111.00001.0001.000pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT3G53070Pericycle00.058800.000000.000000.00158⋯0.001290.0000000.00000-2.1600-2.554.11e-010.54400.9010.981pCVP2_BRI1_Citrine_bri1_T-WT
AT3G08325Pericycle00.058800.000000.000000.00158⋯0.001290.0000000.00000 0.0461-2.553.22e-111.00001.0001.000pCVP2_BRI1_Citrine_bri1_T-bri1_T
AT3G08325Pericycle00.058800.000000.000000.00158⋯0.001290.0000000.00000-2.1600-2.554.11e-010.54400.9010.981pCVP2_BRI1_Citrine_bri1_T-WT
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AT2G19010Pericycle00.000000.000000.096500.00000⋯0.000000.0000000.00125 2.9800-2.558.31e-010.39500.8520.981pCVP2_BRI1_Citrine_bri1_T-WT
AT2G05645Pericycle00.000000.000000.193000.00000⋯0.000000.0000000.00125 3.2100-2.441.36e+000.28600.7560.662pCVP2_BRI1_Citrine_bri1_T-bri1_T
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AT3G20362Pericycle00.000000.000000.772000.00000⋯0.000000.0000000.00999 5.9400-1.916.41e+000.04240.5920.707pCVP2_BRI1_Citrine_bri1_T-WT
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In [29]:
## all genes as background

all_bg <- res_to_write_frq
In [30]:
all_bg %>% filter(gene=="BRI1-mCitrine") %>%
arrange(desc(sc_136.cpm))
A data.frame: 20 × 24
genecluster_idsc_130.cpmsc_134.cpmsc_131.cpmsc_135.cpmsc_132.cpmsc_136.cpmsc_130.frqsc_134.frq⋯WT.frqbri1_T.frqpCVP2_BRI1_Citrine_bri1_T.frqlogFClogCPMFp_valp_adj.locp_adj.glbcontrast
<chr><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><chr>
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BRI1-mCitrineLateral Root Cap000.05150.16303.0803.9400⋯00.001070.0271 5.03 0.3110 60.804.33e-053.70e-032.26e-03pCVP2_BRI1_Citrine_bri1_T-bri1_T
BRI1-mCitrineLateral Root Cap000.05150.16303.0803.9400⋯00.001070.0271 8.67 0.3110 72.802.22e-051.37e-011.29e-02pCVP2_BRI1_Citrine_bri1_T-WT
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BRI1-mCitrineTrichoblast 000.40600.32003.2002.5900⋯00.008710.0862 9.36 0.2080157.007.98e-086.49e-041.56e-04pCVP2_BRI1_Citrine_bri1_T-WT
BRI1-mCitrinePhloem 000.00000.00000.0002.5600⋯00.000000.0139 4.12 0.6840 1.542.15e-019.13e-015.81e-01pCVP2_BRI1_Citrine_bri1_T-bri1_T
BRI1-mCitrinePhloem 000.00000.00000.0002.5600⋯00.000000.0139 4.61 0.6840 1.881.71e-011.00e+009.61e-01pCVP2_BRI1_Citrine_bri1_T-WT
BRI1-mCitrineXylem 000.37500.25700.7942.1800⋯00.006470.0345 1.99-0.0746 2.981.03e-014.84e-013.95e-01pCVP2_BRI1_Citrine_bri1_T-bri1_T
BRI1-mCitrineXylem 000.37500.25700.7942.1800⋯00.006470.0345 5.95-0.0746 11.403.87e-032.38e-012.71e-01pCVP2_BRI1_Citrine_bri1_T-WT
BRI1-mCitrineProcambium 000.00000.00002.7902.1200⋯00.000000.0443 6.67 0.3190 19.701.92e-041.05e-026.44e-03pCVP2_BRI1_Citrine_bri1_T-bri1_T
BRI1-mCitrineProcambium 000.00000.00002.7902.1200⋯00.000000.0443 6.57 0.3190 16.005.76e-048.88e-029.90e-02pCVP2_BRI1_Citrine_bri1_T-WT
BRI1-mCitrinePericycle 000.49700.47802.6602.0300⋯00.011300.0724 2.25 0.2310 19.502.78e-031.41e-014.09e-02pCVP2_BRI1_Citrine_bri1_T-bri1_T
BRI1-mCitrinePericycle 000.49700.47802.6602.0300⋯00.011300.0724 7.82 0.2310 48.401.76e-042.48e-015.04e-02pCVP2_BRI1_Citrine_bri1_T-WT
BRI1-mCitrineCortex 000.20900.19401.4201.6800⋯00.004540.0300 2.89-0.4290 18.502.56e-035.77e-023.87e-02pCVP2_BRI1_Citrine_bri1_T-bri1_T
BRI1-mCitrineCortex 000.20900.19401.4201.6800⋯00.004540.0300 7.55-0.4290 41.301.98e-041.99e-015.42e-02pCVP2_BRI1_Citrine_bri1_T-WT
BRI1-mCitrineEndodermis 000.37600.38900.5081.3600⋯00.008250.0233 1.22-0.7470 2.411.60e-015.36e-015.00e-01pCVP2_BRI1_Citrine_bri1_T-bri1_T
BRI1-mCitrineEndodermis 000.37600.38900.5081.3600⋯00.008250.0233 6.60-0.7470 22.001.62e-034.73e-011.78e-01pCVP2_BRI1_Citrine_bri1_T-WT
In [31]:
length(unique(all_bg$gene))
26993
In [32]:
#total DE genes p_adj.loc < 0.05, abs(logFC) > 1.5
sig_DE <- filter(res_to_write_frq, p_adj.loc<=0.05 & abs(logFC) > log2(1.5))
sig_DE <- left_join(sig_DE, feature_names, by=c("gene"="AGI"))

length(unique(sig_DE$gene))
10281
In [33]:
# filter gene freqs to avoid calling lowly detected genes
sig_DE_fil <- filter(sig_DE, WT.frq >=0.05 | bri1_T.frq >=0.05 | pCVP2_BRI1_Citrine_bri1_T.frq >=0.05)
In [34]:
length(unique(sig_DE_fil$gene))
8694
In [35]:
# load TFs
TF_list <- read_csv("../data/Kay_TF_thalemine_annotations.csv", col_names = c("gene", "TF_Name", "Description"))
Rows: 2485 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): gene, TF_Name, Description

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
In [36]:
sig_DE_fil <- left_join(sig_DE_fil, TF_list)
Joining with `by = join_by(gene)`
In [37]:
# label up vs down
sig_DE_fil <- sig_DE_fil %>%
  mutate(up_dn_label = case_when(logFC >=log2(1.5) ~ "Up",  
                                       logFC <=log2(1/1.5) ~ "Down",
                                       TRUE ~ "Not DE"))

sig_DE_fil$clust_up_dn <- paste(sig_DE_fil$cluster_id, sig_DE_fil$up_dn_label, sep="_")

sig_DE_fil
A data.frame: 15309 × 29
genecluster_idsc_130.cpmsc_134.cpmsc_131.cpmsc_135.cpmsc_132.cpmsc_136.cpmsc_130.frqsc_134.frq⋯Fp_valp_adj.locp_adj.glbcontrastNameTF_NameDescriptionup_dn_labelclust_up_dn
<chr><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><chr><chr><chr><chr><chr><chr>
AT1G01240Columella 31.90 25.20 15.30 14.20 23.00 30.000.20900.1800⋯15.408.59e-052.69e-033.66e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G01240NA NA Up Columella_Up
AT1G01580Columella 41.20 32.80 15.20 18.10 35.50 32.600.25000.2160⋯26.502.70e-072.02e-057.79e-05pCVP2_BRI1_Citrine_bri1_T-bri1_TFRO2 NA NA Up Columella_Up
AT1G01620Columella 623.00 348.00256.00189.00 287.00 386.000.93400.8040⋯21.403.71e-061.97e-044.11e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TPIP1-3 NA NA Up Columella_Up
AT1G01640Columella 10.70 10.50 3.07 6.58 12.20 7.970.06260.0687⋯12.304.50e-041.02e-021.17e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G01640AT1G01640BTB/POZ domain-containing protein Up Columella_Up
AT1G01725Columella 14.90 16.20 4.87 4.28 16.20 18.700.10300.1190⋯43.005.57e-119.57e-097.61e-08pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G01725NA NA Up Columella_Up
AT1G02430Columella 8.99 7.98 5.23 3.29 6.73 11.700.06480.0619⋯10.701.10e-032.06e-022.17e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TATARFD1B NA NA Up Columella_Up
AT1G02610Columella 23.10 23.30 13.50 10.90 23.60 22.600.14500.1580⋯15.309.08e-052.81e-033.80e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G02610NA NA Up Columella_Up
AT1G02860Columella 70.90 71.80 51.40 38.50 77.40 70.300.32700.3280⋯16.504.82e-051.66e-032.43e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TBAH1 NA NA Up Columella_Up
AT1G02870Columella 6.24 17.00 18.60 22.70 11.90 11.700.04250.0945⋯11.606.48e-041.36e-021.51e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G02870NA NA DownColumella_Down
AT1G03030Columella 19.70 19.70 13.20 14.80 19.90 24.900.14000.1370⋯ 8.763.07e-034.53e-024.38e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G03030NA NA Up Columella_Up
AT1G03200Columella 14.20 13.40 4.69 6.91 12.90 13.500.08830.1030⋯17.303.15e-051.17e-031.79e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G03200NA NA Up Columella_Up
AT1G03850Columella 58.40 53.40 42.00 38.50 62.10 88.800.29800.2770⋯25.604.23e-072.97e-051.05e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G03850NA NA Up Columella_Up
AT1G03870Columella 221.00 105.00 95.20 53.00 117.00 139.000.68300.4900⋯29.505.65e-084.88e-062.58e-05pCVP2_BRI1_Citrine_bri1_T-bri1_TFLA9 NA NA Up Columella_Up
AT1G04100Columella 22.20 24.40 36.10 35.60 24.50 21.100.14900.1600⋯11.009.21e-041.80e-021.93e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TIAA10 IAA10 indoleacetic acid-induced protein 10DownColumella_Down
AT1G04180Columella 20.30 20.20 6.13 9.22 11.00 10.000.07820.0893⋯15.101.03e-041.43e-023.56e-02pCVP2_BRI1_Citrine_bri1_T-WT YUC9 NA NA DownColumella_Down
AT1G04280Columella 14.10 13.40 5.23 4.61 14.40 13.400.08830.1010⋯24.706.79e-074.48e-051.45e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G04280NA NA Up Columella_Up
AT1G04870Columella 2.18 2.94 8.30 12.20 3.37 4.080.01680.0223⋯16.704.28e-051.51e-032.24e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TPRMT10 NA NA DownColumella_Down
AT1G05680Columella1080.001030.00730.00738.001180.001110.000.75500.6740⋯29.306.39e-085.49e-062.82e-05pCVP2_BRI1_Citrine_bri1_T-bri1_TUGT74E2 NA NA Up Columella_Up
AT1G05840Columella 7.54 9.24 3.25 5.93 10.40 7.230.05470.0739⋯ 8.603.36e-034.86e-024.65e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G05840NA NA Up Columella_Up
AT1G06090Columella 81.10 124.00 88.70 37.50 144.00 79.900.20200.2290⋯24.507.40e-074.81e-051.52e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G06090NA NA Up Columella_Up
AT1G06330Columella 54.40 46.40 20.20 21.10 34.30 41.700.28000.2440⋯20.805.25e-062.64e-045.20e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G06330NA NA Up Columella_Up
AT1G07030Columella 71.80 58.00 44.70 43.80 65.80 66.800.40100.3570⋯11.208.39e-041.67e-021.81e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G07030NA NA Up Columella_Up
AT1G07070Columella 5.22 9.03 17.00 21.70 10.70 11.700.04020.0704⋯ 9.731.81e-033.02e-023.06e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TRPL35AA NA NA DownColumella_Down
AT1G07160Columella 209.00 149.00113.00116.00 179.00 253.000.55200.4600⋯36.701.38e-091.76e-071.31e-06pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G07160NA NA Up Columella_Up
AT1G07175Columella 158.00 134.00 81.30100.00 141.00 162.000.45900.4020⋯22.402.21e-061.25e-042.95e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G07175NA NA Up Columella_Up
AT1G07290Columella 36.40 29.40 11.70 16.80 20.80 35.200.17900.1620⋯21.503.56e-061.91e-044.00e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TGONST2 NA NA Up Columella_Up
AT1G07930Columella 39.60 49.60 69.30 81.60 49.30 47.800.26400.2660⋯13.202.84e-047.14e-038.47e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TA1 NA NA DownColumella_Down
AT1G08310Columella 7.25 11.10 9.20 2.96 16.80 8.530.05250.0704⋯10.501.17e-032.16e-022.26e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G08310NA NA Up Columella_Up
AT1G08500Columella 35.70 19.10 11.90 9.22 14.10 19.800.21100.1480⋯ 9.232.38e-033.72e-023.67e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TENODL18 NA NA Up Columella_Up
AT1G08890Columella 11.70 13.90 5.05 2.63 8.57 13.500.08270.1050⋯19.908.27e-063.88e-047.02e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TSUGTL4 NA NA Up Columella_Up
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
AT2G17660Pericycle 0.982 1.4700 17.000 15.700 4.6400 2.7000.028000.03160⋯ 55.15.99e-050.03170.002840pCVP2_BRI1_Citrine_bri1_T-bri1_TAT2G17660NA NA DownPericycle_Down
AT2G18620Pericycle 1.720 1.9400 11.100 11.600 2.9100 3.4700.049000.04580⋯ 53.96.52e-050.03270.003030pCVP2_BRI1_Citrine_bri1_T-bri1_TAT2G18620NA NA DownPericycle_Down
AT2G26910Pericycle 3.680 2.3500 10.900 8.450 1.9800 1.4500.083900.06160⋯ 80.51.45e-050.01660.001030pCVP2_BRI1_Citrine_bri1_T-bri1_TABCG32 NA NA DownPericycle_Down
AT2G33790Pericycle 12.800 11.3000 59.400 38.700 20.2000 7.7200.280000.19600⋯ 43.11.46e-040.04110.005280pCVP2_BRI1_Citrine_bri1_T-bri1_TAGP30 NA NA DownPericycle_Down
AT2G33860Pericycle 2.210 1.7000 5.710 7.330 0.8650 1.7400.062900.04420⋯ 41.21.71e-040.04350.005930pCVP2_BRI1_Citrine_bri1_T-bri1_TARF3 ETT Transcriptional factor B3 family protein / auxin-responsive factor AUX/IAA-like proteinDownPericycle_Down
AT2G36640Pericycle 0.000 0.8820 6.640 10.400 1.1100 1.2500.000000.01260⋯ 41.91.61e-040.04320.005700pCVP2_BRI1_Citrine_bri1_T-bri1_TECP63 NA NA DownPericycle_Down
AT2G37540Pericycle 28.500 20.9000 3.540 2.710 11.6000 18.5000.280000.25400⋯ 47.81.00e-040.03860.004090pCVP2_BRI1_Citrine_bri1_T-bri1_TAT2G37540NA NA Up Pericycle_Up
AT2G37870Pericycle 0.982 5.5800 66.300 68.100 7.1700 5.8800.021000.04270⋯ 60.24.32e-050.02940.002260pCVP2_BRI1_Citrine_bri1_T-bri1_TAT2G37870NA NA DownPericycle_Down
AT2G38530Pericycle133.000198.0000110.000 84.100326.0000280.0000.196000.22700⋯ 39.71.95e-040.04580.006530pCVP2_BRI1_Citrine_bri1_T-bri1_TLTP2 NA NA Up Pericycle_Up
AT2G39681Pericycle 10.300 8.8800 4.720 3.750 14.3000 13.8000.189000.14100⋯ 57.35.19e-050.02970.002550pCVP2_BRI1_Citrine_bri1_T-bri1_TTAS2 NA NA Up Pericycle_Up
AT2G39700Pericycle 12.500 10.2000 8.690 4.940 35.5000 19.6000.049000.05530⋯ 74.41.95e-050.01810.001270pCVP2_BRI1_Citrine_bri1_T-bri1_TEXPA4 NA NA Up Pericycle_Up
AT2G45050Pericycle 3.930 2.8200 27.900 17.100 5.3800 4.1500.097900.05850⋯ 95.07.65e-060.01420.000667pCVP2_BRI1_Citrine_bri1_T-bri1_TGATA2 GATA2 GATA transcription factor 2 DownPericycle_Down
AT2G46400Pericycle 28.200 50.7000107.000115.000 38.2000 37.1000.168000.31300⋯ 37.62.36e-040.04970.007440pCVP2_BRI1_Citrine_bri1_T-bri1_TWRKY46 WRKY46WRKY DNA-binding protein 46 DownPericycle_Down
AT2G48010Pericycle 5.400 3.7000 8.260 7.570 2.4100 2.3200.133000.08060⋯ 39.42.01e-040.04650.006650pCVP2_BRI1_Citrine_bri1_T-bri1_TRKF3 NA NA DownPericycle_Down
AT4G01650Pericycle 24.100 39.0000 11.400 9.640 41.6000 39.9000.322000.39200⋯ 54.36.35e-050.03250.002970pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G01650NA NA Up Pericycle_Up
AT4G04223Pericycle 1.720 0.1760 4.780 4.220 0.0000 0.0000.028000.00474⋯ 51.71.43e-040.04090.005210pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G04223NA NA DownPericycle_Down
AT4G11190Pericycle 80.000 64.4000197.000204.000 91.7000 59.8000.385000.27800⋯ 41.51.66e-040.04350.005820pCVP2_BRI1_Citrine_bri1_T-bri1_TDIR13 NA NA DownPericycle_Down
AT4G11211Pericycle250.000269.0000 96.700 84.300338.0000314.0000.825000.83900⋯ 87.81.04e-050.01560.000819pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G11211NA NA Up Pericycle_Up
AT4G12430Pericycle 14.000 14.6000 5.090 4.620 12.3000 12.8000.182000.18800⋯ 38.32.21e-040.04910.007120pCVP2_BRI1_Citrine_bri1_T-bri1_TTPPF NA NA Up Pericycle_Up
AT4G13860Pericycle 8.100 10.6000 6.710 5.820 25.0000 18.6000.154000.18500⋯ 50.98.01e-050.03570.003490pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G13860NA NA Up Pericycle_Up
AT4G15920Pericycle 12.000 13.5000 2.730 1.200 12.4000 16.7000.238000.23400⋯ 58.84.73e-050.02940.002400pCVP2_BRI1_Citrine_bri1_T-bri1_TSWEET17 NA NA Up Pericycle_Up
AT4G16000Pericycle 0.000 0.0588 3.910 3.670 0.0618 0.4820.000000.00158⋯ 38.22.24e-040.04910.007180pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G16000NA NA DownPericycle_Down
AT4G16990Pericycle 12.000 5.8800 0.124 0.159 4.5100 3.0900.224000.12500⋯ 95.07.67e-060.01420.000668pCVP2_BRI1_Citrine_bri1_T-bri1_TRLM3 NA NA Up Pericycle_Up
AT4G22214Pericycle 16.400 12.2000 55.200 32.400 17.1000 14.1000.315000.20100⋯ 45.51.20e-040.03860.004620pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G22214NA NA DownPericycle_Down
AT4G23450Pericycle 11.800 15.3000 4.970 4.940 16.4000 17.8000.182000.22300⋯ 63.33.59e-050.02700.001980pCVP2_BRI1_Citrine_bri1_T-bri1_TAIRP1 NA NA Up Pericycle_Up
AT4G23680Pericycle 0.245 0.7050 5.900 3.430 0.3090 0.3860.006990.00632⋯ 50.38.35e-050.03650.003590pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G23680NA NA DownPericycle_Down
AT4G30660Pericycle 8.840 10.2000 69.500 52.900 16.8000 23.4000.224000.20400⋯ 41.11.73e-040.04350.005980pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G30660NA NA DownPericycle_Down
AT4G36880Pericycle 24.100 23.6000 63.600 51.900 26.6000 23.0000.322000.27600⋯ 37.52.39e-040.05000.007510pCVP2_BRI1_Citrine_bri1_T-bri1_TCP1 NA NA DownPericycle_Down
AT4G39940Pericycle 0.736 1.3500 20.100 16.000 5.5600 2.8900.021000.02530⋯ 46.91.07e-040.03860.004290pCVP2_BRI1_Citrine_bri1_T-bri1_TAPK2 NA NA DownPericycle_Down
AT4G39950Pericycle 2.700 2.5300 57.000 49.300 9.8300 5.1100.028000.04900⋯124.02.71e-060.01020.000338pCVP2_BRI1_Citrine_bri1_T-bri1_TCYP79B2 NA NA DownPericycle_Down
In [38]:
sig_DE_fil
write.csv(sig_DE_fil, file = "../output/CVP/pCVP2_Citrine_celltype_EdgeR_q0.05_FC1.5_r_v_4_20240429.csv")
A data.frame: 15309 × 29
genecluster_idsc_130.cpmsc_134.cpmsc_131.cpmsc_135.cpmsc_132.cpmsc_136.cpmsc_130.frqsc_134.frq⋯Fp_valp_adj.locp_adj.glbcontrastNameTF_NameDescriptionup_dn_labelclust_up_dn
<chr><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><chr><chr><chr><chr><chr><chr>
AT1G01240Columella 31.90 25.20 15.30 14.20 23.00 30.000.20900.1800⋯15.408.59e-052.69e-033.66e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G01240NA NA Up Columella_Up
AT1G01580Columella 41.20 32.80 15.20 18.10 35.50 32.600.25000.2160⋯26.502.70e-072.02e-057.79e-05pCVP2_BRI1_Citrine_bri1_T-bri1_TFRO2 NA NA Up Columella_Up
AT1G01620Columella 623.00 348.00256.00189.00 287.00 386.000.93400.8040⋯21.403.71e-061.97e-044.11e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TPIP1-3 NA NA Up Columella_Up
AT1G01640Columella 10.70 10.50 3.07 6.58 12.20 7.970.06260.0687⋯12.304.50e-041.02e-021.17e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G01640AT1G01640BTB/POZ domain-containing protein Up Columella_Up
AT1G01725Columella 14.90 16.20 4.87 4.28 16.20 18.700.10300.1190⋯43.005.57e-119.57e-097.61e-08pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G01725NA NA Up Columella_Up
AT1G02430Columella 8.99 7.98 5.23 3.29 6.73 11.700.06480.0619⋯10.701.10e-032.06e-022.17e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TATARFD1B NA NA Up Columella_Up
AT1G02610Columella 23.10 23.30 13.50 10.90 23.60 22.600.14500.1580⋯15.309.08e-052.81e-033.80e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G02610NA NA Up Columella_Up
AT1G02860Columella 70.90 71.80 51.40 38.50 77.40 70.300.32700.3280⋯16.504.82e-051.66e-032.43e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TBAH1 NA NA Up Columella_Up
AT1G02870Columella 6.24 17.00 18.60 22.70 11.90 11.700.04250.0945⋯11.606.48e-041.36e-021.51e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G02870NA NA DownColumella_Down
AT1G03030Columella 19.70 19.70 13.20 14.80 19.90 24.900.14000.1370⋯ 8.763.07e-034.53e-024.38e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G03030NA NA Up Columella_Up
AT1G03200Columella 14.20 13.40 4.69 6.91 12.90 13.500.08830.1030⋯17.303.15e-051.17e-031.79e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G03200NA NA Up Columella_Up
AT1G03850Columella 58.40 53.40 42.00 38.50 62.10 88.800.29800.2770⋯25.604.23e-072.97e-051.05e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G03850NA NA Up Columella_Up
AT1G03870Columella 221.00 105.00 95.20 53.00 117.00 139.000.68300.4900⋯29.505.65e-084.88e-062.58e-05pCVP2_BRI1_Citrine_bri1_T-bri1_TFLA9 NA NA Up Columella_Up
AT1G04100Columella 22.20 24.40 36.10 35.60 24.50 21.100.14900.1600⋯11.009.21e-041.80e-021.93e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TIAA10 IAA10 indoleacetic acid-induced protein 10DownColumella_Down
AT1G04180Columella 20.30 20.20 6.13 9.22 11.00 10.000.07820.0893⋯15.101.03e-041.43e-023.56e-02pCVP2_BRI1_Citrine_bri1_T-WT YUC9 NA NA DownColumella_Down
AT1G04280Columella 14.10 13.40 5.23 4.61 14.40 13.400.08830.1010⋯24.706.79e-074.48e-051.45e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G04280NA NA Up Columella_Up
AT1G04870Columella 2.18 2.94 8.30 12.20 3.37 4.080.01680.0223⋯16.704.28e-051.51e-032.24e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TPRMT10 NA NA DownColumella_Down
AT1G05680Columella1080.001030.00730.00738.001180.001110.000.75500.6740⋯29.306.39e-085.49e-062.82e-05pCVP2_BRI1_Citrine_bri1_T-bri1_TUGT74E2 NA NA Up Columella_Up
AT1G05840Columella 7.54 9.24 3.25 5.93 10.40 7.230.05470.0739⋯ 8.603.36e-034.86e-024.65e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G05840NA NA Up Columella_Up
AT1G06090Columella 81.10 124.00 88.70 37.50 144.00 79.900.20200.2290⋯24.507.40e-074.81e-051.52e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G06090NA NA Up Columella_Up
AT1G06330Columella 54.40 46.40 20.20 21.10 34.30 41.700.28000.2440⋯20.805.25e-062.64e-045.20e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G06330NA NA Up Columella_Up
AT1G07030Columella 71.80 58.00 44.70 43.80 65.80 66.800.40100.3570⋯11.208.39e-041.67e-021.81e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G07030NA NA Up Columella_Up
AT1G07070Columella 5.22 9.03 17.00 21.70 10.70 11.700.04020.0704⋯ 9.731.81e-033.02e-023.06e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TRPL35AA NA NA DownColumella_Down
AT1G07160Columella 209.00 149.00113.00116.00 179.00 253.000.55200.4600⋯36.701.38e-091.76e-071.31e-06pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G07160NA NA Up Columella_Up
AT1G07175Columella 158.00 134.00 81.30100.00 141.00 162.000.45900.4020⋯22.402.21e-061.25e-042.95e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G07175NA NA Up Columella_Up
AT1G07290Columella 36.40 29.40 11.70 16.80 20.80 35.200.17900.1620⋯21.503.56e-061.91e-044.00e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TGONST2 NA NA Up Columella_Up
AT1G07930Columella 39.60 49.60 69.30 81.60 49.30 47.800.26400.2660⋯13.202.84e-047.14e-038.47e-03pCVP2_BRI1_Citrine_bri1_T-bri1_TA1 NA NA DownColumella_Down
AT1G08310Columella 7.25 11.10 9.20 2.96 16.80 8.530.05250.0704⋯10.501.17e-032.16e-022.26e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TAT1G08310NA NA Up Columella_Up
AT1G08500Columella 35.70 19.10 11.90 9.22 14.10 19.800.21100.1480⋯ 9.232.38e-033.72e-023.67e-02pCVP2_BRI1_Citrine_bri1_T-bri1_TENODL18 NA NA Up Columella_Up
AT1G08890Columella 11.70 13.90 5.05 2.63 8.57 13.500.08270.1050⋯19.908.27e-063.88e-047.02e-04pCVP2_BRI1_Citrine_bri1_T-bri1_TSUGTL4 NA NA Up Columella_Up
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AT2G17660Pericycle 0.982 1.4700 17.000 15.700 4.6400 2.7000.028000.03160⋯ 55.15.99e-050.03170.002840pCVP2_BRI1_Citrine_bri1_T-bri1_TAT2G17660NA NA DownPericycle_Down
AT2G18620Pericycle 1.720 1.9400 11.100 11.600 2.9100 3.4700.049000.04580⋯ 53.96.52e-050.03270.003030pCVP2_BRI1_Citrine_bri1_T-bri1_TAT2G18620NA NA DownPericycle_Down
AT2G26910Pericycle 3.680 2.3500 10.900 8.450 1.9800 1.4500.083900.06160⋯ 80.51.45e-050.01660.001030pCVP2_BRI1_Citrine_bri1_T-bri1_TABCG32 NA NA DownPericycle_Down
AT2G33790Pericycle 12.800 11.3000 59.400 38.700 20.2000 7.7200.280000.19600⋯ 43.11.46e-040.04110.005280pCVP2_BRI1_Citrine_bri1_T-bri1_TAGP30 NA NA DownPericycle_Down
AT2G33860Pericycle 2.210 1.7000 5.710 7.330 0.8650 1.7400.062900.04420⋯ 41.21.71e-040.04350.005930pCVP2_BRI1_Citrine_bri1_T-bri1_TARF3 ETT Transcriptional factor B3 family protein / auxin-responsive factor AUX/IAA-like proteinDownPericycle_Down
AT2G36640Pericycle 0.000 0.8820 6.640 10.400 1.1100 1.2500.000000.01260⋯ 41.91.61e-040.04320.005700pCVP2_BRI1_Citrine_bri1_T-bri1_TECP63 NA NA DownPericycle_Down
AT2G37540Pericycle 28.500 20.9000 3.540 2.710 11.6000 18.5000.280000.25400⋯ 47.81.00e-040.03860.004090pCVP2_BRI1_Citrine_bri1_T-bri1_TAT2G37540NA NA Up Pericycle_Up
AT2G37870Pericycle 0.982 5.5800 66.300 68.100 7.1700 5.8800.021000.04270⋯ 60.24.32e-050.02940.002260pCVP2_BRI1_Citrine_bri1_T-bri1_TAT2G37870NA NA DownPericycle_Down
AT2G38530Pericycle133.000198.0000110.000 84.100326.0000280.0000.196000.22700⋯ 39.71.95e-040.04580.006530pCVP2_BRI1_Citrine_bri1_T-bri1_TLTP2 NA NA Up Pericycle_Up
AT2G39681Pericycle 10.300 8.8800 4.720 3.750 14.3000 13.8000.189000.14100⋯ 57.35.19e-050.02970.002550pCVP2_BRI1_Citrine_bri1_T-bri1_TTAS2 NA NA Up Pericycle_Up
AT2G39700Pericycle 12.500 10.2000 8.690 4.940 35.5000 19.6000.049000.05530⋯ 74.41.95e-050.01810.001270pCVP2_BRI1_Citrine_bri1_T-bri1_TEXPA4 NA NA Up Pericycle_Up
AT2G45050Pericycle 3.930 2.8200 27.900 17.100 5.3800 4.1500.097900.05850⋯ 95.07.65e-060.01420.000667pCVP2_BRI1_Citrine_bri1_T-bri1_TGATA2 GATA2 GATA transcription factor 2 DownPericycle_Down
AT2G46400Pericycle 28.200 50.7000107.000115.000 38.2000 37.1000.168000.31300⋯ 37.62.36e-040.04970.007440pCVP2_BRI1_Citrine_bri1_T-bri1_TWRKY46 WRKY46WRKY DNA-binding protein 46 DownPericycle_Down
AT2G48010Pericycle 5.400 3.7000 8.260 7.570 2.4100 2.3200.133000.08060⋯ 39.42.01e-040.04650.006650pCVP2_BRI1_Citrine_bri1_T-bri1_TRKF3 NA NA DownPericycle_Down
AT4G01650Pericycle 24.100 39.0000 11.400 9.640 41.6000 39.9000.322000.39200⋯ 54.36.35e-050.03250.002970pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G01650NA NA Up Pericycle_Up
AT4G04223Pericycle 1.720 0.1760 4.780 4.220 0.0000 0.0000.028000.00474⋯ 51.71.43e-040.04090.005210pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G04223NA NA DownPericycle_Down
AT4G11190Pericycle 80.000 64.4000197.000204.000 91.7000 59.8000.385000.27800⋯ 41.51.66e-040.04350.005820pCVP2_BRI1_Citrine_bri1_T-bri1_TDIR13 NA NA DownPericycle_Down
AT4G11211Pericycle250.000269.0000 96.700 84.300338.0000314.0000.825000.83900⋯ 87.81.04e-050.01560.000819pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G11211NA NA Up Pericycle_Up
AT4G12430Pericycle 14.000 14.6000 5.090 4.620 12.3000 12.8000.182000.18800⋯ 38.32.21e-040.04910.007120pCVP2_BRI1_Citrine_bri1_T-bri1_TTPPF NA NA Up Pericycle_Up
AT4G13860Pericycle 8.100 10.6000 6.710 5.820 25.0000 18.6000.154000.18500⋯ 50.98.01e-050.03570.003490pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G13860NA NA Up Pericycle_Up
AT4G15920Pericycle 12.000 13.5000 2.730 1.200 12.4000 16.7000.238000.23400⋯ 58.84.73e-050.02940.002400pCVP2_BRI1_Citrine_bri1_T-bri1_TSWEET17 NA NA Up Pericycle_Up
AT4G16000Pericycle 0.000 0.0588 3.910 3.670 0.0618 0.4820.000000.00158⋯ 38.22.24e-040.04910.007180pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G16000NA NA DownPericycle_Down
AT4G16990Pericycle 12.000 5.8800 0.124 0.159 4.5100 3.0900.224000.12500⋯ 95.07.67e-060.01420.000668pCVP2_BRI1_Citrine_bri1_T-bri1_TRLM3 NA NA Up Pericycle_Up
AT4G22214Pericycle 16.400 12.2000 55.200 32.400 17.1000 14.1000.315000.20100⋯ 45.51.20e-040.03860.004620pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G22214NA NA DownPericycle_Down
AT4G23450Pericycle 11.800 15.3000 4.970 4.940 16.4000 17.8000.182000.22300⋯ 63.33.59e-050.02700.001980pCVP2_BRI1_Citrine_bri1_T-bri1_TAIRP1 NA NA Up Pericycle_Up
AT4G23680Pericycle 0.245 0.7050 5.900 3.430 0.3090 0.3860.006990.00632⋯ 50.38.35e-050.03650.003590pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G23680NA NA DownPericycle_Down
AT4G30660Pericycle 8.840 10.2000 69.500 52.900 16.8000 23.4000.224000.20400⋯ 41.11.73e-040.04350.005980pCVP2_BRI1_Citrine_bri1_T-bri1_TAT4G30660NA NA DownPericycle_Down
AT4G36880Pericycle 24.100 23.6000 63.600 51.900 26.6000 23.0000.322000.27600⋯ 37.52.39e-040.05000.007510pCVP2_BRI1_Citrine_bri1_T-bri1_TCP1 NA NA DownPericycle_Down
AT4G39940Pericycle 0.736 1.3500 20.100 16.000 5.5600 2.8900.021000.02530⋯ 46.91.07e-040.03860.004290pCVP2_BRI1_Citrine_bri1_T-bri1_TAPK2 NA NA DownPericycle_Down
AT4G39950Pericycle 2.700 2.5300 57.000 49.300 9.8300 5.1100.028000.04900⋯124.02.71e-060.01020.000338pCVP2_BRI1_Citrine_bri1_T-bri1_TCYP79B2 NA NA DownPericycle_Down
In [39]:
# add DE and up/dn to total list
sig_to_join <- sig_DE_fil %>%
mutate(clust_gene=paste(contrast, cluster_id, gene, sep="_")) %>%
select(clust_gene, up_dn_label, clust_up_dn)
In [40]:
all_bg <- mutate(all_bg, clust_gene=paste(contrast, cluster_id, gene, sep="_"))
In [41]:
all_bg <- left_join(all_bg, feature_names, by=c("gene"="AGI"))
In [42]:
all_bg$DE <- all_bg$clust_gene %in% sig_to_join$clust_gene
In [43]:
all_bg <- all_bg %>%
left_join(sig_to_join, by="clust_gene") %>%
arrange(all_bg, p_adj.loc)
In [44]:
write.csv(all_bg, file = "../output/CVP/all_genes_pCVP2_Citrine_celltype_EdgeR_q0.05_FC1.5_r_v_4_20240429.csv")

Plotting¶

In [45]:
rc.integrated$geno <- factor(rc.integrated$geno, 
                             levels=c("WT", "bri1_T", "pCVP2_BRI1_Citrine_bri1_T"))
In [46]:
options(repr.plot.width=16.5, repr.plot.height=6)
(Celltype_umap <- DimPlot(rc.integrated, 
                      reduction = "umap", 
                      group.by = "cell_type", 
                      cols = color, split.by = 'geno', 
                      ncol=3, 
                      pt.size = 0.5))

ggsave("../output/CVP/Cell_type_umap_square.pdf", width=16.5, height=6)
In [47]:
options(repr.plot.width = 18, repr.plot.height = 6)

DefaultAssay(rc.integrated) <- "SCT"
# expression of the transgene
FeaturePlot(rc.integrated, features="BRI1-mCitrine", split.by = "geno", order=T, max.cutoff = "q90", pt.size = 0.5)

ggsave("../output/CVP/BRI1-mCitrine_expression.pdf", width=18.3, height=6)
Warning message in FeaturePlot(rc.integrated, features = "BRI1-mCitrine", split.by = "geno", :
“All cells have the same value (0) of BRI1-mCitrine.”
In [48]:
# expression of CVP2
options(repr.plot.width = 18, repr.plot.height = 6)

DefaultAssay(rc.integrated) <- "SCT"
# expression of the transgene
FeaturePlot(rc.integrated, features="AT1G05470", split.by = "geno", order=T, max.cutoff = "q90", pt.size = 0.5)

ggsave("../output/CVP/CVP2_AT1G05470_expression.pdf", width=18.3, height=6)
In [ ]: